Hey everyone! Thrilled to be launching Spykio on Product Hunt today!
We created Spykio to solve a problem we faced constantly: vector databases are good, but often not smart enough for sophisticated LLM applications. They tell you what's similar, but struggle when you need what's actually relevant.
Spykio takes a different approach using dynamic knowledge graphs. It analyzes your documents (PDFs, DOCX, etc. – no chunking needed!) to understand the underlying concepts and connections. When you query, it uses this graph to find information based on semantic understanding, not just vector distance. Take for example law; a prompt based similarity search will not find the applicable law for your question.
The goal? More accurate results, less frustration tuning RAG pipelines, and ultimately, better AI applications. We've focused on making it super easy to integrate via our SDKs (Node, Python, TS) or REST API.
Curious to know:
Are you currently using RAG? What are the biggest challenges?
How important is retrieval accuracy vs. speed for your application? (We offer modes for both!)
What kind of documents are you working with?
Ask us anything! We're here all day to chat and answer questions. Looking forward to your feedback! 😊
Honestly, Spykio is a game-changer. At my company, we've been building legal AI-tools using RAG. We noticed RAG does allow the AI to understand some law, but wouldn't be able to actually retrieve the right law and answer questions accurately based on relevant law. So we looked towards Spykio to solve this, and guess what? It did ;)
Revealio: Discover & Connect